We propose a robust framework for the planar pose graph optimization contaminated by loop closure outliers. Our framework rejects outliers by first decoupling the robust PGO problem wrapped by a Truncated Least Squares kernel into two subproblems. Then, the framework introduces a linear angle representation to rewrite the first subproblem that is originally formulated with rotation matrices. The framework is configured with the Graduated Non-Convexity (GNC) algorithm to solve the two non-convex subproblems in succession without initial guesses. Thanks to the linearity properties of both the subproblems, our framework requires only linear solvers to optimally solve the optimization problems encountered in GNC. We extensively validate the proposed framework, named DEGNC-LAF (DEcoupled Graduated Non-Convexity with Linear Angle Formulation) in planar PGO benchmarks. It turns out that it runs significantly (sometimes up to over 30 times) faster than the standard and general-purpose GNC while resulting in high-quality estimates.

翻译：我们提出一个强大的平面图示优化框架, 由循环闭合离子器污染。 我们的框架通过首先解开由圆圈最小方内核包裹的强势 PGO 问题, 将问题分为两个子问题, 从而拒绝异常点 。 然后, 框架引入了一个线性角度的表达方式, 以重写第一个子问题, 最初用旋转矩阵来制定 。 框架配置为非共性毕业算法( GNC ), 以解决继承中的两个非共性子问题, 而不进行初步猜测 。 由于这两个子问题的直线性性质, 我们的框架只需要线性解答器来最佳地解决 GNC 遇到的优化问题 。 我们广泛验证了在PGO 基准中拟议的框架, 名为 DEGNC- LAF ( 脱coupleded Genconticle Convention) 。 。 框架与 Linear Angle Progle Production 相比, 其运行速度( 有时超过30倍) 大大快于标准和通用的通用的通用 GNCNCNC, 同时得出高质量的估计 。